Abstract:

Fitness assignment of individuals and diversity maintenance of population are two key techniques of evolutionary algorithms. First, on the one hand, this paper introduces some related concepts of Pareto εdominance which can determine the strength Pareto values of the individuals of population, according to the strength Pareto values of individuals, some better individuals are selected into the offspring population by the technique of Pareto ranking; on the other hand, in order to maintain the diversity of population, a crowdeddensity method is introduced to remove some individuals that are located in the crowed regions. Then, according to some characteristics of differential evolution (DE), through using the appropriate DE strategies and control parameters, this paper proposes a differential evolution algorithm for multiobjective optimization, which is called DEAMO. Finally, numerical experiments show that DEAMO can perform well when tested on several benchmark multiobjective optimization problems.

Key words: multiobjective optimization;differential evolution;evolutionary algorithm